Coyote Killing: Where Species and Identities Collide
Bibliographic record
Abstract
Abstract Although predator killing is a global phenomenon, few studies interrogate the individual and societal drivers of choosing lethal versus non-lethal actions towards coyotes. Results here derive from 48 in situ , semi-structured interviews conducted during 2015–2017 with rural residential and agricultural landowners in the Foothills Parkland Region of Alberta, Canada. Interviews recorded landowner experiences with coyotes, and their perceptions, values, beliefs, animal husbandry practices, and actions towards coyotes. Invoking a critical geography perspective and grounded theory methods, we found the practice of coyote killing and anti-coyote sentiments to be deeply entangled with and mutually reconstituted by constructs of “masculinity,” “rurality,” and colonial settler identity. Coyote killing also appeared as a form of discursive power, arising from urban-rural tensions. Finally, geographies of local history, family, and community intersected with identity, gendered-labor, and power – placing coyotes in a vicious and ongoing cycle of oppression and violence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".